Accommodating Ontologies to Biological Reality—Top-Level Categories of Cumulative-Constitutively Organized Material Entities

BACKGROUND: The Basic Formal Ontology (BFO) is a top-level formal foundational ontology for the biomedical domain. It has been developed with the purpose to serve as an ontologically consistent template for top-level categories of application oriented and domain reference ontologies within the Open Biological and Biomedical Ontologies Foundry (OBO). BFO is important for enabling OBO ontologies to facilitate in reliably communicating and managing data and metadata within and across biomedical databases. Following its intended single inheritance policy, BFO's three top-level categories of material entity (i.e. ‘object’, ‘fiat object part’, ‘object aggregate’) must be exhaustive and mutually disjoint. We have shown elsewhere that for accommodating all types of constitutively organized material entities, BFO must be extended by additional categories of material entity. METHODOLOGY/PRINCIPAL FINDINGS: Unfortunately, most biomedical material entities are cumulative-constitutively organized. We show that even the extended BFO does not exhaustively cover cumulative-constitutively organized material entities. We provide examples from biology and everyday life that demonstrate the necessity for ‘portion of matter’ as another material building block. This implies the necessity for further extending BFO by ‘portion of matter’ as well as three additional categories that possess portions of matter as aggregate components. These extensions are necessary if the basic assumption that all parts that share the same granularity level exhaustively sum to the whole should also apply to cumulative-constitutively organized material entities. By suggesting a notion of granular representation we provide a way to maintain the single inheritance principle when dealing with cumulative-constitutively organized material entities. CONCLUSIONS/SIGNIFICANCE: We suggest to extend BFO to incorporate additional categories of material entity and to rearrange its top-level material entity taxonomy. With these additions and the notion of granular representation, BFO would exhaustively cover all top-level types of material entities that application oriented ontologies may use as templates, while still maintaining the single inheritance principle

Movement of pulsed resource subsidies from kelp forests to deep fjords

Resource subsidies in the form of allochthonous primary production drive secondary production in many ecosystems, often sustaining diversity and overall productivity. Despite their importance in structuring marine communities, there is little understanding of how subsidies move through juxtaposed habitats and into recipient communities. We investigated the transport of detritus from kelp forests to a deep Arctic fjord (northern Norway). We quantified the seasonal abundance and size structure of kelp detritus in shallow subtidal (0‒12 m), deep subtidal (12‒85 m), and deep fjord (400‒450 m) habitats using a combination of camera surveys, dive observations, and detritus collections over 1 year. Detritus formed dense accumulations in habitats adjacent to kelp forests, and the timing of depositions coincided with the discrete loss of whole kelp blades during spring. We tracked these blades through the deep subtidal and into the deep fjord, and showed they act as a short-term resource pulse transported over several weeks. In deep subtidal regions, detritus consisted mostly of fragments and its depth distribution was similar across seasons (50% of total observations). Tagged pieces of detritus moved slowly out of kelp forests (displaced 4‒50 m (mean 11.8 m ± 8.5 SD) in 11‒17 days, based on minimum estimates from recovered pieces), and most (75%) variability in the rate of export was related to wave exposure and substrate. Tight resource coupling between kelp forests and deep fjords indicate that changes in kelp abundance would propagate through to deep fjord ecosystems, with likely consequences for the ecosystem functioning and services they provide.acceptedVersio

False positive reduction in protein-protein interaction predictions using gene ontology annotations

<p>Abstract</p> <p>Background</p> <p>Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.</p> <p>Results</p> <p>Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The '<it>strength</it>', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the <it>strength </it>varies between two and ten-fold of randomly removing protein pairs from the datasets.</p> <p>Conclusion</p> <p>Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.</p

Mouse anatomy ontologies:enhancements and tools for exploring and integrating biomedical data

Mouse anatomy ontologies provide standard nomenclature for describing normal and mutant mouse anatomy, and are essential for the description and integration of data directly related to anatomy such as gene expression patterns. Building on our previous work on anatomical ontologies for the embryonic and adult mouse, we have recently developed a new and substantially revised anatomical ontology covering all life stages of the mouse. Anatomical terms are organized in complex hierarchies enabling multiple relationships between terms. Tissue classification as well as partonomic, developmental, and other types of relationships can be represented. Hierarchies for specific developmental stages can also be derived. The ontology forms the core of the eMouse Atlas Project (EMAP) and is used extensively for annotating and integrating gene expression patterns and other data by the Gene Expression Database (GXD), the eMouse Atlas of Gene Expression (EMAGE) and other database resources. Here we illustrate the evolution of the developmental and adult mouse anatomical ontologies toward one combined system. We report on recent ontology enhancements, describe the current status, and discuss future plans for mouse anatomy ontology development and application in integrating data resources. Mamm Genome 2015 Oct; 26(9-10):422-3

NONNEGATIVE TENSOR FACTORIZATION FOR SOURCE SEPARATION OF LOOPS IN AUDIO

The prevalence of exact repetition in loop-based music makes it an opportune target for source separation. Nonnegative factorization approaches have been used to model the repetition of looped content, and kernel additive modeling has leveraged periodicity within a piece to separate looped background elements. We propose a novel method of leveraging periodicity in a factorization model: we treat the two-dimensional spectrogram as a three-dimensional tensor, and use nonnegative tensor factorization to estimate the component spectral templates, rhythms and loop recurrences in a single step. Testing our method on synthesized loop-based examples, we find that our algorithm mostly exceeds the performance of competing methods, with a reduction in execution cost. We discuss limitations of the algorithm as we demonstrate its potential to analyze larger and more complex songs

Design and creation of a large-scale database of structural annotations.

This paper describes the design and creation of an unprecedentedly large database of over 2400 structural annotations of nearly 1400 musical recordings. The database is intended to be a test set for algorithms that will be used to analyze a much larger corpus of hundreds of thousands of recordings, as part of the Structural Analysis of Large Amounts of Musical Information (SALAMI) project. This paper describes the design goals of the database and the practical issues that were encountered during its creation. In particular, we discuss the selection of the recordings, the development of an annotation format and procedure that adapts work by Peeters and Deruty [10], and the management and execution of the project. We also summarize some of the properties of the resulting corpus of annotations, including average inter-annotator agreement. © 2011 International Society for Music Information Retrieval

Design and creation of a large-scale database of structural annotations.

This paper describes the design and creation of an unprecedentedly large database of over 2400 structural annotations of nearly 1400 musical recordings. The database is intended to be a test set for algorithms that will be used to analyze a much larger corpus of hundreds of thousands of recordings, as part of the Structural Analysis of Large Amounts of Musical Information (SALAMI) project. This paper describes the design goals of the database and the practical issues that were encountered during its creation. In particular, we discuss the selection of the recordings, the development of an annotation format and procedure that adapts work by Peeters and Deruty [10], and the management and execution of the project. We also summarize some of the properties of the resulting corpus of annotations, including average inter-annotator agreement. © 2011 International Society for Music Information Retrieval